Abstract

Many digital special effects require knowledge of the motion present in an image sequence. In order for these effects to be realistic, blending seamlessly with unmodified live action or animation, motion must be represented accurately. Most existing methods of motion estimation are unsuitable for use in postproduction for one or more reasons; namely poor accuracy; corruption, by aliasing and the aperture problem, of estimation of large-magnitude motion; failure to handle multiple motions and motion boundaries; representation of curvilinear motion as concatenated translations instead of as smooth curves; slowness of execution and inefficiency in the presence of small variations between successive images. Novel methods of motion estimation are proposed here that are specifically designed for use in postproduction and address all of the above problems. The techniques are based on parametric estimation of optical-flow fields, reformulated in terms of displacements rather than velocities. The paradigm of displacement estimation leads to techniques for iterative updating of motion estimation for accuracy; faster motion estimation by exploiting redundancies between successive images; representation of motion over a sequence of images with a single set of parameters; and curvilinear representation of motion. Robust statistics provides a means for distinguishing separate types of motion and overcoming the problems of motion boundaries. Accurate recovery of the motion of the background in a sequence, combined with other image characteristics, leads to a segmentation procedure that greatly accelerates the rotoscoping and compositing tasks commonly carried out in postproduction. Comparative evaluation of the proposed methods with other techniques for motion estimation and image segmentation indicates that, in most cases, the new work provides considerable improvements in quality.